Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations44256
Missing cells289474
Missing cells (%)23.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 MiB
Average record size in memory224.0 B

Variable types

Numeric18
Text1
Categorical7
Unsupported2

Alerts

city_name has constant value "Toronto" Constant
lat has constant value "43.653226" Constant
lon has constant value "-79.383184" Constant
clouds_all is highly overall correlated with weather_description and 1 other fieldsHigh correlation
dew_point is highly overall correlated with feels_like and 4 other fieldsHigh correlation
feels_like is highly overall correlated with dew_point and 4 other fieldsHigh correlation
humidity is highly overall correlated with rain_3h and 1 other fieldsHigh correlation
rain_1h is highly overall correlated with rain_3h and 1 other fieldsHigh correlation
rain_3h is highly overall correlated with humidity and 1 other fieldsHigh correlation
snow_1h is highly overall correlated with snow_3h and 1 other fieldsHigh correlation
snow_3h is highly overall correlated with humidity and 4 other fieldsHigh correlation
temp is highly overall correlated with dew_point and 4 other fieldsHigh correlation
temp_max is highly overall correlated with dew_point and 4 other fieldsHigh correlation
temp_min is highly overall correlated with dew_point and 4 other fieldsHigh correlation
timezone is highly overall correlated with dew_point and 4 other fieldsHigh correlation
weather_description is highly overall correlated with clouds_all and 4 other fieldsHigh correlation
weather_icon is highly overall correlated with clouds_all and 3 other fieldsHigh correlation
weather_id is highly overall correlated with rain_1h and 5 other fieldsHigh correlation
weather_main is highly overall correlated with snow_3h and 3 other fieldsHigh correlation
wind_gust is highly overall correlated with wind_speedHigh correlation
wind_speed is highly overall correlated with wind_gustHigh correlation
visibility has 9162 (20.7%) missing values Missing
sea_level has 44256 (100.0%) missing values Missing
grnd_level has 44256 (100.0%) missing values Missing
wind_gust has 23716 (53.6%) missing values Missing
rain_1h has 36961 (83.5%) missing values Missing
rain_3h has 43924 (99.2%) missing values Missing
snow_1h has 42993 (97.1%) missing values Missing
snow_3h has 44206 (99.9%) missing values Missing
dt is uniformly distributed Uniform
sea_level is an unsupported type, check if it needs cleaning or further analysis Unsupported
grnd_level is an unsupported type, check if it needs cleaning or further analysis Unsupported
wind_speed has 530 (1.2%) zeros Zeros
wind_deg has 1514 (3.4%) zeros Zeros
wind_gust has 2343 (5.3%) zeros Zeros
clouds_all has 12685 (28.7%) zeros Zeros

Reproduction

Analysis started2024-10-20 01:25:50.375905
Analysis finished2024-10-20 01:26:04.962869
Duration14.59 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

dt
Real number (ℝ)

Uniform 

Distinct43104
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6265355 × 109
Minimum1.5488928 × 109
Maximum1.7040636 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:05.023575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.5488928 × 109
5-th percentile1.5565851 × 109
Q11.5873903 × 109
median1.6265754 × 109
Q31.6657461 × 109
95-th percentile1.6965081 × 109
Maximum1.7040636 × 109
Range1.551708 × 108
Interquartile range (IQR)78355800

Descriptive statistics

Standard deviation44942076
Coefficient of variation (CV)0.027630554
Kurtosis-1.2071806
Mean1.6265355 × 109
Median Absolute Deviation (MAD)39178800
Skewness-0.00069374225
Sum7.1983954 × 1013
Variance2.0197902 × 1015
MonotonicityIncreasing
2024-10-19T21:26:05.078662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1676595600 3
 
< 0.1%
1626400800 3
 
< 0.1%
1609848000 3
 
< 0.1%
1692939600 3
 
< 0.1%
1631084400 3
 
< 0.1%
1550718000 3
 
< 0.1%
1582027200 3
 
< 0.1%
1550721600 3
 
< 0.1%
1689242400 3
 
< 0.1%
1692932400 3
 
< 0.1%
Other values (43094) 44226
99.9%
ValueCountFrequency (%)
1548892800 1
< 0.1%
1548896400 1
< 0.1%
1548900000 1
< 0.1%
1548903600 1
< 0.1%
1548907200 1
< 0.1%
1548910800 1
< 0.1%
1548914400 1
< 0.1%
1548918000 1
< 0.1%
1548921600 1
< 0.1%
1548925200 1
< 0.1%
ValueCountFrequency (%)
1704063600 2
< 0.1%
1704060000 2
< 0.1%
1704056400 1
< 0.1%
1704052800 2
< 0.1%
1704049200 2
< 0.1%
1704045600 1
< 0.1%
1704042000 1
< 0.1%
1704038400 1
< 0.1%
1704034800 1
< 0.1%
1704031200 1
< 0.1%

dt_iso
Text

Distinct43104
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:05.221118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters1283424
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41983 ?
Unique (%)94.9%

Sample

1st row2019-01-31 00:00:00 +0000 UTC
2nd row2019-01-31 01:00:00 +0000 UTC
3rd row2019-01-31 02:00:00 +0000 UTC
4th row2019-01-31 03:00:00 +0000 UTC
5th row2019-01-31 04:00:00 +0000 UTC
ValueCountFrequency (%)
0000 44256
25.0%
utc 44256
25.0%
11:00:00 1853
 
1.0%
09:00:00 1852
 
1.0%
12:00:00 1852
 
1.0%
08:00:00 1852
 
1.0%
02:00:00 1851
 
1.0%
10:00:00 1850
 
1.0%
18:00:00 1848
 
1.0%
19:00:00 1847
 
1.0%
Other values (1812) 73707
41.6%
2024-10-19T21:26:05.407413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 485221
37.8%
132768
 
10.3%
2 128329
 
10.0%
- 88512
 
6.9%
: 88512
 
6.9%
1 79230
 
6.2%
U 44256
 
3.4%
C 44256
 
3.4%
T 44256
 
3.4%
+ 44256
 
3.4%
Other values (7) 103828
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1283424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 485221
37.8%
132768
 
10.3%
2 128329
 
10.0%
- 88512
 
6.9%
: 88512
 
6.9%
1 79230
 
6.2%
U 44256
 
3.4%
C 44256
 
3.4%
T 44256
 
3.4%
+ 44256
 
3.4%
Other values (7) 103828
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1283424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 485221
37.8%
132768
 
10.3%
2 128329
 
10.0%
- 88512
 
6.9%
: 88512
 
6.9%
1 79230
 
6.2%
U 44256
 
3.4%
C 44256
 
3.4%
T 44256
 
3.4%
+ 44256
 
3.4%
Other values (7) 103828
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1283424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 485221
37.8%
132768
 
10.3%
2 128329
 
10.0%
- 88512
 
6.9%
: 88512
 
6.9%
1 79230
 
6.2%
U 44256
 
3.4%
C 44256
 
3.4%
T 44256
 
3.4%
+ 44256
 
3.4%
Other values (7) 103828
 
8.1%

timezone
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size345.9 KiB
-14400
29146 
-18000
15110 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters265536
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-18000
2nd row-18000
3rd row-18000
4th row-18000
5th row-18000

Common Values

ValueCountFrequency (%)
-14400 29146
65.9%
-18000 15110
34.1%

Length

2024-10-19T21:26:05.462961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T21:26:05.498076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
14400 29146
65.9%
18000 15110
34.1%

Most occurring characters

ValueCountFrequency (%)
0 103622
39.0%
4 58292
22.0%
- 44256
16.7%
1 44256
16.7%
8 15110
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 265536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 103622
39.0%
4 58292
22.0%
- 44256
16.7%
1 44256
16.7%
8 15110
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 265536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 103622
39.0%
4 58292
22.0%
- 44256
16.7%
1 44256
16.7%
8 15110
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 265536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 103622
39.0%
4 58292
22.0%
- 44256
16.7%
1 44256
16.7%
8 15110
 
5.7%

city_name
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size345.9 KiB
Toronto
44256 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters309792
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowToronto
2nd rowToronto
3rd rowToronto
4th rowToronto
5th rowToronto

Common Values

ValueCountFrequency (%)
Toronto 44256
100.0%

Length

2024-10-19T21:26:05.535468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T21:26:05.569052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
toronto 44256
100.0%

Most occurring characters

ValueCountFrequency (%)
o 132768
42.9%
T 44256
 
14.3%
r 44256
 
14.3%
n 44256
 
14.3%
t 44256
 
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 309792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 132768
42.9%
T 44256
 
14.3%
r 44256
 
14.3%
n 44256
 
14.3%
t 44256
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 309792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 132768
42.9%
T 44256
 
14.3%
r 44256
 
14.3%
n 44256
 
14.3%
t 44256
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 309792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 132768
42.9%
T 44256
 
14.3%
r 44256
 
14.3%
n 44256
 
14.3%
t 44256
 
14.3%

lat
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size345.9 KiB
43.653226
44256 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters398304
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43.653226
2nd row43.653226
3rd row43.653226
4th row43.653226
5th row43.653226

Common Values

ValueCountFrequency (%)
43.653226 44256
100.0%

Length

2024-10-19T21:26:05.604559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T21:26:05.637938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
43.653226 44256
100.0%

Most occurring characters

ValueCountFrequency (%)
3 88512
22.2%
6 88512
22.2%
2 88512
22.2%
4 44256
11.1%
. 44256
11.1%
5 44256
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 398304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 88512
22.2%
6 88512
22.2%
2 88512
22.2%
4 44256
11.1%
. 44256
11.1%
5 44256
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 398304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 88512
22.2%
6 88512
22.2%
2 88512
22.2%
4 44256
11.1%
. 44256
11.1%
5 44256
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 398304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 88512
22.2%
6 88512
22.2%
2 88512
22.2%
4 44256
11.1%
. 44256
11.1%
5 44256
11.1%

lon
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size345.9 KiB
-79.383184
44256 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters442560
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-79.383184
2nd row-79.383184
3rd row-79.383184
4th row-79.383184
5th row-79.383184

Common Values

ValueCountFrequency (%)
-79.383184 44256
100.0%

Length

2024-10-19T21:26:05.673137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T21:26:05.706341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
79.383184 44256
100.0%

Most occurring characters

ValueCountFrequency (%)
3 88512
20.0%
8 88512
20.0%
- 44256
10.0%
7 44256
10.0%
9 44256
10.0%
. 44256
10.0%
1 44256
10.0%
4 44256
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 442560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 88512
20.0%
8 88512
20.0%
- 44256
10.0%
7 44256
10.0%
9 44256
10.0%
. 44256
10.0%
1 44256
10.0%
4 44256
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 442560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 88512
20.0%
8 88512
20.0%
- 44256
10.0%
7 44256
10.0%
9 44256
10.0%
. 44256
10.0%
1 44256
10.0%
4 44256
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 442560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 88512
20.0%
8 88512
20.0%
- 44256
10.0%
7 44256
10.0%
9 44256
10.0%
. 44256
10.0%
1 44256
10.0%
4 44256
10.0%

temp
Real number (ℝ)

High correlation 

Distinct4553
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.28196
Minimum-20.54
Maximum34.77
Zeros23
Zeros (%)0.1%
Negative6921
Negative (%)15.6%
Memory size345.9 KiB
2024-10-19T21:26:05.745712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-20.54
5-th percentile-5.34
Q12.24
median9.7
Q319.04
95-th percentile25.66
Maximum34.77
Range55.31
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation10.00777
Coefficient of variation (CV)0.97333296
Kurtosis-0.85518712
Mean10.28196
Median Absolute Deviation (MAD)8.2
Skewness-0.050281446
Sum455038.41
Variance100.15547
MonotonicityNot monotonic
2024-10-19T21:26:05.792465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03 44
 
0.1%
1.02 44
 
0.1%
1.87 42
 
0.1%
0.95 41
 
0.1%
1.97 40
 
0.1%
0.83 39
 
0.1%
2.03 38
 
0.1%
1.8 38
 
0.1%
1.83 37
 
0.1%
1.86 37
 
0.1%
Other values (4543) 43856
99.1%
ValueCountFrequency (%)
-20.54 1
< 0.1%
-20.39 1
< 0.1%
-20.38 1
< 0.1%
-20.35 1
< 0.1%
-20.28 1
< 0.1%
-20.25 1
< 0.1%
-20.24 1
< 0.1%
-20.21 1
< 0.1%
-20.07 1
< 0.1%
-19.99 1
< 0.1%
ValueCountFrequency (%)
34.77 1
< 0.1%
34.62 1
< 0.1%
34.47 1
< 0.1%
34.35 1
< 0.1%
34.28 1
< 0.1%
34 1
< 0.1%
33.94 1
< 0.1%
33.91 1
< 0.1%
33.87 1
< 0.1%
33.86 1
< 0.1%

visibility
Real number (ℝ)

Missing 

Distinct396
Distinct (%)1.1%
Missing9162
Missing (%)20.7%
Infinite0
Infinite (%)0.0%
Mean8884.1324
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:05.842110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2816
Q110000
median10000
Q310000
95-th percentile10000
Maximum10000
Range9999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2468.8338
Coefficient of variation (CV)0.27789251
Kurtosis3.6614694
Mean8884.1324
Median Absolute Deviation (MAD)0
Skewness-2.2063055
Sum3.1177974 × 108
Variance6095140.4
MonotonicityNot monotonic
2024-10-19T21:26:05.892879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27245
61.6%
9656 512
 
1.2%
4828 489
 
1.1%
8047 437
 
1.0%
6437 370
 
0.8%
4023 154
 
0.3%
3219 111
 
0.3%
402 99
 
0.2%
2816 96
 
0.2%
8046 85
 
0.2%
Other values (386) 5496
 
12.4%
(Missing) 9162
 
20.7%
ValueCountFrequency (%)
1 39
0.1%
2 26
0.1%
3 27
0.1%
4 23
0.1%
5 20
< 0.1%
6 16
< 0.1%
7 12
 
< 0.1%
8 7
 
< 0.1%
9 11
 
< 0.1%
10 7
 
< 0.1%
ValueCountFrequency (%)
10000 27245
61.6%
9656 512
 
1.2%
9000 2
 
< 0.1%
8086 1
 
< 0.1%
8081 1
 
< 0.1%
8071 1
 
< 0.1%
8070 1
 
< 0.1%
8066 1
 
< 0.1%
8064 5
 
< 0.1%
8061 2
 
< 0.1%

dew_point
Real number (ℝ)

High correlation 

Distinct4226
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7343538
Minimum-26.69
Maximum24.49
Zeros22
Zeros (%)< 0.1%
Negative15057
Negative (%)34.0%
Memory size345.9 KiB
2024-10-19T21:26:05.941717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-26.69
5-th percentile-10.6625
Q1-2.06
median4.2
Q312.69
95-th percentile19.14
Maximum24.49
Range51.18
Interquartile range (IQR)14.75

Descriptive statistics

Standard deviation9.3186992
Coefficient of variation (CV)1.9683149
Kurtosis-0.69015364
Mean4.7343538
Median Absolute Deviation (MAD)7.24
Skewness-0.13907448
Sum209523.56
Variance86.838154
MonotonicityNot monotonic
2024-10-19T21:26:05.989595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.07 35
 
0.1%
-0.31 35
 
0.1%
1.39 34
 
0.1%
-1.31 33
 
0.1%
0.51 31
 
0.1%
2.95 31
 
0.1%
-0.93 31
 
0.1%
1.52 30
 
0.1%
1.62 30
 
0.1%
1.79 30
 
0.1%
Other values (4216) 43936
99.3%
ValueCountFrequency (%)
-26.69 1
< 0.1%
-26.53 1
< 0.1%
-26.28 1
< 0.1%
-26.27 1
< 0.1%
-26.14 1
< 0.1%
-26.1 1
< 0.1%
-26.07 1
< 0.1%
-26.04 1
< 0.1%
-25.95 1
< 0.1%
-25.83 1
< 0.1%
ValueCountFrequency (%)
24.49 1
< 0.1%
24.24 1
< 0.1%
24.06 2
< 0.1%
24.05 1
< 0.1%
23.9 1
< 0.1%
23.81 1
< 0.1%
23.72 1
< 0.1%
23.68 2
< 0.1%
23.64 1
< 0.1%
23.62 1
< 0.1%

feels_like
Real number (ℝ)

High correlation 

Distinct5336
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0260855
Minimum-27.54
Maximum37.67
Zeros16
Zeros (%)< 0.1%
Negative13864
Negative (%)31.3%
Memory size345.9 KiB
2024-10-19T21:26:06.036860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-27.54
5-th percentile-11.56
Q1-2.05
median8.425
Q318.83
95-th percentile25.8525
Maximum37.67
Range65.21
Interquartile range (IQR)20.88

Descriptive statistics

Standard deviation12.203441
Coefficient of variation (CV)1.5204724
Kurtosis-0.96220163
Mean8.0260855
Median Absolute Deviation (MAD)10.435
Skewness-0.11635359
Sum355202.44
Variance148.92398
MonotonicityNot monotonic
2024-10-19T21:26:06.087313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.01 29
 
0.1%
-3.5 28
 
0.1%
21.89 27
 
0.1%
-4.97 26
 
0.1%
19.72 26
 
0.1%
21.27 26
 
0.1%
-5.17 26
 
0.1%
-2.37 25
 
0.1%
-1.95 25
 
0.1%
-4.1 25
 
0.1%
Other values (5326) 43993
99.4%
ValueCountFrequency (%)
-27.54 1
< 0.1%
-27.39 1
< 0.1%
-27.38 1
< 0.1%
-27.35 1
< 0.1%
-27.28 1
< 0.1%
-27.24 1
< 0.1%
-27.21 1
< 0.1%
-27.18 1
< 0.1%
-27.07 1
< 0.1%
-26.99 1
< 0.1%
ValueCountFrequency (%)
37.67 1
< 0.1%
36.85 1
< 0.1%
36.82 1
< 0.1%
36.56 1
< 0.1%
36.52 1
< 0.1%
36.37 1
< 0.1%
36.35 1
< 0.1%
36.1 1
< 0.1%
36.05 1
< 0.1%
36.03 2
< 0.1%

temp_min
Real number (ℝ)

High correlation 

Distinct1448
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2571719
Minimum-23.12
Maximum34.02
Zeros0
Zeros (%)0.0%
Negative8613
Negative (%)19.5%
Memory size345.9 KiB
2024-10-19T21:26:06.135828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-23.12
5-th percentile-6.68
Q11.32
median8.59
Q317.91
95-th percentile24.7
Maximum34.02
Range57.14
Interquartile range (IQR)16.59

Descriptive statistics

Standard deviation10.051613
Coefficient of variation (CV)1.085819
Kurtosis-0.81764144
Mean9.2571719
Median Absolute Deviation (MAD)8.12
Skewness-0.063775913
Sum409685.4
Variance101.03493
MonotonicityNot monotonic
2024-10-19T21:26:06.183472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.55 359
 
0.8%
4.89 319
 
0.7%
19.89 295
 
0.7%
0.47 290
 
0.7%
5.89 288
 
0.7%
2.55 287
 
0.6%
1.32 267
 
0.6%
20.89 266
 
0.6%
6.89 266
 
0.6%
21.89 265
 
0.6%
Other values (1438) 41354
93.4%
ValueCountFrequency (%)
-23.12 2
 
< 0.1%
-22.57 1
 
< 0.1%
-22.45 1
 
< 0.1%
-22.24 1
 
< 0.1%
-22.15 1
 
< 0.1%
-22.01 1
 
< 0.1%
-21.46 1
 
< 0.1%
-21.45 5
< 0.1%
-21.2 1
 
< 0.1%
-21.13 1
 
< 0.1%
ValueCountFrequency (%)
34.02 1
 
< 0.1%
33.96 1
 
< 0.1%
33.89 1
 
< 0.1%
33.82 1
 
< 0.1%
33.41 1
 
< 0.1%
33.27 1
 
< 0.1%
33.25 1
 
< 0.1%
32.89 1
 
< 0.1%
32.76 3
< 0.1%
32.71 2
< 0.1%

temp_max
Real number (ℝ)

High correlation 

Distinct1494
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.170995
Minimum-20.07
Maximum35.56
Zeros0
Zeros (%)0.0%
Negative6025
Negative (%)13.6%
Memory size345.9 KiB
2024-10-19T21:26:06.230284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-20.07
5-th percentile-4.85
Q12.93
median10.56
Q320.07
95-th percentile27.14
Maximum35.56
Range55.63
Interquartile range (IQR)17.14

Descriptive statistics

Standard deviation10.27712
Coefficient of variation (CV)0.91998248
Kurtosis-0.86548756
Mean11.170995
Median Absolute Deviation (MAD)8.41
Skewness-0.032832508
Sum494383.55
Variance105.61919
MonotonicityNot monotonic
2024-10-19T21:26:06.277879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.15 583
 
1.3%
3.15 568
 
1.3%
5.15 507
 
1.1%
1.15 502
 
1.1%
0.15 464
 
1.0%
2.15 439
 
1.0%
-0.85 342
 
0.8%
9.15 342
 
0.8%
8.15 310
 
0.7%
10.15 296
 
0.7%
Other values (1484) 39903
90.2%
ValueCountFrequency (%)
-20.07 4
< 0.1%
-19.93 1
 
< 0.1%
-19.85 2
 
< 0.1%
-19.73 1
 
< 0.1%
-19.45 1
 
< 0.1%
-19.32 4
< 0.1%
-19.07 5
< 0.1%
-18.94 1
 
< 0.1%
-18.85 1
 
< 0.1%
-18.81 2
 
< 0.1%
ValueCountFrequency (%)
35.56 1
 
< 0.1%
35.55 2
 
< 0.1%
35.26 1
 
< 0.1%
35.15 2
 
< 0.1%
34.7 1
 
< 0.1%
34.56 6
< 0.1%
34.55 3
< 0.1%
34.52 1
 
< 0.1%
34.36 2
 
< 0.1%
34.15 5
< 0.1%

pressure
Real number (ℝ)

Distinct67
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1016.0306
Minimum981
Maximum1048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:06.326018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum981
5-th percentile1003
Q11011
median1016
Q31021
95-th percentile1029
Maximum1048
Range67
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.0384127
Coefficient of variation (CV)0.0079115853
Kurtosis0.27384373
Mean1016.0306
Median Absolute Deviation (MAD)5
Skewness-0.020865686
Sum44965450
Variance64.616079
MonotonicityNot monotonic
2024-10-19T21:26:06.377132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1016 2374
 
5.4%
1017 2348
 
5.3%
1015 2243
 
5.1%
1018 2226
 
5.0%
1014 2212
 
5.0%
1013 2155
 
4.9%
1012 2136
 
4.8%
1019 2096
 
4.7%
1011 1933
 
4.4%
1020 1801
 
4.1%
Other values (57) 22732
51.4%
ValueCountFrequency (%)
981 3
 
< 0.1%
982 2
 
< 0.1%
984 4
 
< 0.1%
985 2
 
< 0.1%
986 14
< 0.1%
987 8
 
< 0.1%
988 11
< 0.1%
989 15
< 0.1%
990 26
0.1%
991 21
< 0.1%
ValueCountFrequency (%)
1048 3
 
< 0.1%
1047 3
 
< 0.1%
1046 1
 
< 0.1%
1045 2
 
< 0.1%
1044 2
 
< 0.1%
1043 7
 
< 0.1%
1042 14
 
< 0.1%
1041 22
< 0.1%
1040 31
0.1%
1039 40
0.1%

sea_level
Unsupported

Missing  Rejected  Unsupported 

Missing44256
Missing (%)100.0%
Memory size345.9 KiB

grnd_level
Unsupported

Missing  Rejected  Unsupported 

Missing44256
Missing (%)100.0%
Memory size345.9 KiB

humidity
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.858482
Minimum15
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:06.428673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile41
Q159
median71
Q382
95-th percentile93
Maximum100
Range85
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.880898
Coefficient of variation (CV)0.22732956
Kurtosis-0.36672329
Mean69.858482
Median Absolute Deviation (MAD)11
Skewness-0.43961878
Sum3091657
Variance252.20293
MonotonicityNot monotonic
2024-10-19T21:26:06.476421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 1133
 
2.6%
69 1106
 
2.5%
72 1105
 
2.5%
71 1078
 
2.4%
74 1077
 
2.4%
73 1061
 
2.4%
66 1015
 
2.3%
67 1014
 
2.3%
76 1001
 
2.3%
77 997
 
2.3%
Other values (76) 33669
76.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
16 4
 
< 0.1%
17 4
 
< 0.1%
18 3
 
< 0.1%
19 5
 
< 0.1%
20 2
 
< 0.1%
21 8
 
< 0.1%
22 10
 
< 0.1%
23 14
< 0.1%
24 26
0.1%
ValueCountFrequency (%)
100 5
 
< 0.1%
99 11
 
< 0.1%
98 68
 
0.2%
97 182
 
0.4%
96 418
0.9%
95 561
1.3%
94 761
1.7%
93 818
1.8%
92 750
1.7%
91 755
1.7%

wind_speed
Real number (ℝ)

High correlation  Zeros 

Distinct585
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4245982
Minimum0
Maximum21.6
Zeros530
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:06.524438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.89
Q12.1
median3.6
Q36.2
95-th percentile10.29
Maximum21.6
Range21.6
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation2.9817007
Coefficient of variation (CV)0.67389186
Kurtosis0.805581
Mean4.4245982
Median Absolute Deviation (MAD)2.06
Skewness0.95731719
Sum195815.02
Variance8.8905393
MonotonicityNot monotonic
2024-10-19T21:26:06.574187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 2709
 
6.1%
4.12 1653
 
3.7%
0.89 1630
 
3.7%
1.34 1618
 
3.7%
4.63 1544
 
3.5%
5.14 1488
 
3.4%
7.2 1442
 
3.3%
0.45 1426
 
3.2%
3.1 1323
 
3.0%
3.09 1213
 
2.7%
Other values (575) 28210
63.7%
ValueCountFrequency (%)
0 530
1.2%
0.07 1
 
< 0.1%
0.11 3
 
< 0.1%
0.14 1
 
< 0.1%
0.17 2
 
< 0.1%
0.18 1
 
< 0.1%
0.19 2
 
< 0.1%
0.2 2
 
< 0.1%
0.24 1
 
< 0.1%
0.25 1
 
< 0.1%
ValueCountFrequency (%)
21.6 1
 
< 0.1%
20.06 1
 
< 0.1%
19.55 1
 
< 0.1%
19.03 2
 
< 0.1%
19 3
 
< 0.1%
18.52 1
 
< 0.1%
18.5 2
 
< 0.1%
18.01 4
< 0.1%
17.5 4
< 0.1%
17.49 8
< 0.1%

wind_deg
Real number (ℝ)

Zeros 

Distinct361
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.73529
Minimum0
Maximum360
Zeros1514
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:06.622243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q180
median221
Q3290
95-th percentile333
Maximum360
Range360
Interquartile range (IQR)210

Descriptive statistics

Standard deviation105.84824
Coefficient of variation (CV)0.54077237
Kurtosis-1.291785
Mean195.73529
Median Absolute Deviation (MAD)89
Skewness-0.30372083
Sum8662461
Variance11203.849
MonotonicityNot monotonic
2024-10-19T21:26:06.672958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 2430
 
5.5%
60 2423
 
5.5%
320 1963
 
4.4%
310 1760
 
4.0%
300 1689
 
3.8%
270 1659
 
3.7%
290 1605
 
3.6%
80 1597
 
3.6%
280 1592
 
3.6%
0 1514
 
3.4%
Other values (351) 26024
58.8%
ValueCountFrequency (%)
0 1514
3.4%
1 25
 
0.1%
2 14
 
< 0.1%
3 13
 
< 0.1%
4 17
 
< 0.1%
5 45
 
0.1%
6 15
 
< 0.1%
7 11
 
< 0.1%
8 17
 
< 0.1%
9 18
 
< 0.1%
ValueCountFrequency (%)
360 339
0.8%
359 12
 
< 0.1%
358 25
 
0.1%
357 27
 
0.1%
356 17
 
< 0.1%
355 57
 
0.1%
354 24
 
0.1%
353 18
 
< 0.1%
352 31
 
0.1%
351 27
 
0.1%

wind_gust
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct116
Distinct (%)0.6%
Missing23716
Missing (%)53.6%
Infinite0
Infinite (%)0.0%
Mean7.7362381
Minimum0
Maximum32.9
Zeros2343
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:06.723260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.58
median8.23
Q311.32
95-th percentile15.95
Maximum32.9
Range32.9
Interquartile range (IQR)7.74

Descriptive statistics

Standard deviation4.9787839
Coefficient of variation (CV)0.64356653
Kurtosis-0.6260404
Mean7.7362381
Median Absolute Deviation (MAD)3.76
Skewness0.11941652
Sum158902.33
Variance24.788289
MonotonicityNot monotonic
2024-10-19T21:26:06.772358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2343
 
5.3%
7.72 822
 
1.9%
10.8 817
 
1.8%
5.36 665
 
1.5%
3.58 640
 
1.4%
8.23 611
 
1.4%
1.34 606
 
1.4%
3.13 592
 
1.3%
4.02 576
 
1.3%
10.29 573
 
1.3%
Other values (106) 12295
27.8%
(Missing) 23716
53.6%
ValueCountFrequency (%)
0 2343
5.3%
0.45 16
 
< 0.1%
0.89 253
 
0.6%
1.34 606
 
1.4%
1.79 199
 
0.4%
2.24 410
 
0.9%
2.68 512
 
1.2%
3.13 592
 
1.3%
3.58 640
 
1.4%
4.02 576
 
1.3%
ValueCountFrequency (%)
32.9 1
 
< 0.1%
25.72 2
< 0.1%
25.21 2
< 0.1%
25.2 1
 
< 0.1%
24.69 1
 
< 0.1%
24.2 3
< 0.1%
24.18 2
< 0.1%
23.66 2
< 0.1%
23.2 1
 
< 0.1%
23.15 1
 
< 0.1%

rain_1h
Real number (ℝ)

High correlation  Missing 

Distinct446
Distinct (%)6.1%
Missing36961
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean0.97335298
Minimum0.1
Maximum28.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:06.820569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.13
Q10.25
median0.5
Q31.02
95-th percentile3.403
Maximum28.7
Range28.6
Interquartile range (IQR)0.77

Descriptive statistics

Standard deviation1.5535062
Coefficient of variation (CV)1.5960358
Kurtosis44.146918
Mean0.97335298
Median Absolute Deviation (MAD)0.27
Skewness5.192797
Sum7100.61
Variance2.4133814
MonotonicityNot monotonic
2024-10-19T21:26:06.869845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 1276
 
2.9%
0.51 628
 
1.4%
0.23 341
 
0.8%
0.76 165
 
0.4%
0.28 160
 
0.4%
1.02 143
 
0.3%
0.38 143
 
0.3%
0.1 120
 
0.3%
0.11 104
 
0.2%
0.12 104
 
0.2%
Other values (436) 4111
 
9.3%
(Missing) 36961
83.5%
ValueCountFrequency (%)
0.1 120
0.3%
0.11 104
0.2%
0.12 104
0.2%
0.13 91
0.2%
0.14 84
0.2%
0.15 66
0.1%
0.16 58
0.1%
0.17 60
0.1%
0.18 67
0.2%
0.19 62
0.1%
ValueCountFrequency (%)
28.7 1
< 0.1%
24.89 1
< 0.1%
17.78 2
< 0.1%
15.41 1
< 0.1%
15.12 2
< 0.1%
14.48 2
< 0.1%
14.23 1
< 0.1%
14.05 1
< 0.1%
13.97 1
< 0.1%
13.12 2
< 0.1%

rain_3h
Real number (ℝ)

High correlation  Missing 

Distinct23
Distinct (%)6.9%
Missing43924
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean1.8716867
Minimum0.2
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:06.913881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.2
Q10.3
median0.6
Q32
95-th percentile7
Maximum21
Range20.8
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation3.2506536
Coefficient of variation (CV)1.7367509
Kurtosis15.880637
Mean1.8716867
Median Absolute Deviation (MAD)0.4
Skewness3.6397349
Sum621.4
Variance10.566749
MonotonicityNot monotonic
2024-10-19T21:26:06.952611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.2 73
 
0.2%
0.5 34
 
0.1%
0.3 32
 
0.1%
3 25
 
0.1%
0.7 24
 
0.1%
2 23
 
0.1%
0.4 23
 
0.1%
1 18
 
< 0.1%
0.6 13
 
< 0.1%
4 11
 
< 0.1%
Other values (13) 56
 
0.1%
(Missing) 43924
99.2%
ValueCountFrequency (%)
0.2 73
0.2%
0.3 32
0.1%
0.4 23
 
0.1%
0.5 34
0.1%
0.6 13
 
< 0.1%
0.7 24
 
0.1%
0.8 9
 
< 0.1%
0.9 8
 
< 0.1%
1 18
 
< 0.1%
2 23
 
0.1%
ValueCountFrequency (%)
21 2
 
< 0.1%
20 3
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
12 1
 
< 0.1%
10 4
 
< 0.1%
9 2
 
< 0.1%
8 1
 
< 0.1%
7 5
< 0.1%
6 10
< 0.1%

snow_1h
Real number (ℝ)

High correlation  Missing 

Distinct124
Distinct (%)9.8%
Missing42993
Missing (%)97.1%
Infinite0
Infinite (%)0.0%
Mean0.53068884
Minimum0.1
Maximum11.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:06.997652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.11
Q10.2
median0.26
Q30.51
95-th percentile1.74
Maximum11.18
Range11.08
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.79376657
Coefficient of variation (CV)1.4957288
Kurtosis62.671518
Mean0.53068884
Median Absolute Deviation (MAD)0.13
Skewness6.511843
Sum670.26
Variance0.63006537
MonotonicityNot monotonic
2024-10-19T21:26:07.048529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 176
 
0.4%
0.51 138
 
0.3%
0.23 76
 
0.2%
0.2 56
 
0.1%
0.1 53
 
0.1%
0.28 40
 
0.1%
0.11 38
 
0.1%
0.12 37
 
0.1%
0.13 31
 
0.1%
0.76 30
 
0.1%
Other values (114) 588
 
1.3%
(Missing) 42993
97.1%
ValueCountFrequency (%)
0.1 53
0.1%
0.11 38
0.1%
0.12 37
0.1%
0.13 31
0.1%
0.14 26
0.1%
0.15 20
 
< 0.1%
0.16 27
0.1%
0.17 19
 
< 0.1%
0.18 18
 
< 0.1%
0.19 12
 
< 0.1%
ValueCountFrequency (%)
11.18 1
< 0.1%
9.91 1
< 0.1%
8.89 1
< 0.1%
7.37 2
< 0.1%
6.1 1
< 0.1%
4.57 1
< 0.1%
4.32 1
< 0.1%
4.1 1
< 0.1%
4.06 1
< 0.1%
3.81 2
< 0.1%

snow_3h
Real number (ℝ)

High correlation  Missing 

Distinct13
Distinct (%)26.0%
Missing44206
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean1.3
Minimum0.2
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:07.089300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.2
Q10.4
median0.85
Q31.75
95-th percentile5.1
Maximum6
Range5.8
Interquartile range (IQR)1.35

Descriptive statistics

Standard deviation1.4773114
Coefficient of variation (CV)1.1363934
Kurtosis4.3762911
Mean1.3
Median Absolute Deviation (MAD)0.45
Skewness2.1624069
Sum65
Variance2.182449
MonotonicityNot monotonic
2024-10-19T21:26:07.126539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 8
 
< 0.1%
0.2 7
 
< 0.1%
2 6
 
< 0.1%
0.4 6
 
< 0.1%
0.9 4
 
< 0.1%
3 3
 
< 0.1%
0.3 3
 
< 0.1%
6 3
 
< 0.1%
0.7 3
 
< 0.1%
0.6 3
 
< 0.1%
Other values (3) 4
 
< 0.1%
(Missing) 44206
99.9%
ValueCountFrequency (%)
0.2 7
< 0.1%
0.3 3
 
< 0.1%
0.4 6
< 0.1%
0.5 2
 
< 0.1%
0.6 3
 
< 0.1%
0.7 3
 
< 0.1%
0.8 1
 
< 0.1%
0.9 4
< 0.1%
1 8
< 0.1%
2 6
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
4 1
 
< 0.1%
3 3
 
< 0.1%
2 6
< 0.1%
1 8
< 0.1%
0.9 4
< 0.1%
0.8 1
 
< 0.1%
0.7 3
 
< 0.1%
0.6 3
 
< 0.1%
0.5 2
 
< 0.1%

clouds_all
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.013625
Minimum0
Maximum100
Zeros12685
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:07.172589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median75
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)100

Descriptive statistics

Standard deviation43.987267
Coefficient of variation (CV)0.77152202
Kurtosis-1.7190519
Mean57.013625
Median Absolute Deviation (MAD)25
Skewness-0.29457849
Sum2523195
Variance1934.8797
MonotonicityNot monotonic
2024-10-19T21:26:07.224405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 17556
39.7%
0 12685
28.7%
75 3934
 
8.9%
20 1691
 
3.8%
40 1685
 
3.8%
99 384
 
0.9%
98 322
 
0.7%
97 208
 
0.5%
96 168
 
0.4%
2 168
 
0.4%
Other values (91) 5455
 
12.3%
ValueCountFrequency (%)
0 12685
28.7%
1 158
 
0.4%
2 168
 
0.4%
3 94
 
0.2%
4 91
 
0.2%
5 92
 
0.2%
6 74
 
0.2%
7 74
 
0.2%
8 60
 
0.1%
9 82
 
0.2%
ValueCountFrequency (%)
100 17556
39.7%
99 384
 
0.9%
98 322
 
0.7%
97 208
 
0.5%
96 168
 
0.4%
95 150
 
0.3%
94 141
 
0.3%
93 131
 
0.3%
92 105
 
0.2%
91 101
 
0.2%

weather_id
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean740.46247
Minimum200
Maximum804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size345.9 KiB
2024-10-19T21:26:07.266867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile500
Q1800
median800
Q3804
95-th percentile804
Maximum804
Range604
Interquartile range (IQR)4

Descriptive statistics

Standard deviation116.27747
Coefficient of variation (CV)0.15703357
Kurtosis1.1240406
Mean740.46247
Median Absolute Deviation (MAD)3
Skewness-1.5898262
Sum32769907
Variance13520.449
MonotonicityNot monotonic
2024-10-19T21:26:07.307983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
800 12962
29.3%
804 11263
25.4%
500 5446
12.3%
803 4416
 
10.0%
802 2585
 
5.8%
801 2217
 
5.0%
701 1609
 
3.6%
600 1485
 
3.4%
501 1312
 
3.0%
601 384
 
0.9%
Other values (9) 577
 
1.3%
ValueCountFrequency (%)
200 33
 
0.1%
201 16
 
< 0.1%
202 11
 
< 0.1%
211 50
 
0.1%
500 5446
12.3%
501 1312
 
3.0%
502 183
 
0.4%
503 3
 
< 0.1%
600 1485
 
3.4%
601 384
 
0.9%
ValueCountFrequency (%)
804 11263
25.4%
803 4416
 
10.0%
802 2585
 
5.8%
801 2217
 
5.0%
800 12962
29.3%
741 128
 
0.3%
721 142
 
0.3%
701 1609
 
3.6%
602 11
 
< 0.1%
601 384
 
0.9%

weather_main
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size345.9 KiB
Clouds
20481 
Clear
12962 
Rain
6944 
Snow
 
1880
Mist
 
1609
Other values (3)
 
380

Length

Max length12
Median length6
Mean length5.2354483
Min length3

Characters and Unicode

Total characters231700
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSnow
2nd rowSnow
3rd rowSnow
4th rowClouds
5th rowClear

Common Values

ValueCountFrequency (%)
Clouds 20481
46.3%
Clear 12962
29.3%
Rain 6944
 
15.7%
Snow 1880
 
4.2%
Mist 1609
 
3.6%
Haze 142
 
0.3%
Fog 128
 
0.3%
Thunderstorm 110
 
0.2%

Length

2024-10-19T21:26:07.353126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-19T21:26:07.396285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
clouds 20481
46.3%
clear 12962
29.3%
rain 6944
 
15.7%
snow 1880
 
4.2%
mist 1609
 
3.6%
haze 142
 
0.3%
fog 128
 
0.3%
thunderstorm 110
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 33443
14.4%
l 33443
14.4%
o 22599
9.8%
s 22200
9.6%
u 20591
8.9%
d 20591
8.9%
a 20048
8.7%
e 13214
 
5.7%
r 13182
 
5.7%
n 8934
 
3.9%
Other values (13) 23455
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 231700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 33443
14.4%
l 33443
14.4%
o 22599
9.8%
s 22200
9.6%
u 20591
8.9%
d 20591
8.9%
a 20048
8.7%
e 13214
 
5.7%
r 13182
 
5.7%
n 8934
 
3.9%
Other values (13) 23455
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 231700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 33443
14.4%
l 33443
14.4%
o 22599
9.8%
s 22200
9.6%
u 20591
8.9%
d 20591
8.9%
a 20048
8.7%
e 13214
 
5.7%
r 13182
 
5.7%
n 8934
 
3.9%
Other values (13) 23455
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 231700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 33443
14.4%
l 33443
14.4%
o 22599
9.8%
s 22200
9.6%
u 20591
8.9%
d 20591
8.9%
a 20048
8.7%
e 13214
 
5.7%
r 13182
 
5.7%
n 8934
 
3.9%
Other values (13) 23455
10.1%

weather_description
Categorical

High correlation 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size345.9 KiB
sky is clear
12962 
overcast clouds
11263 
light rain
5446 
broken clouds
4416 
scattered clouds
2585 
Other values (14)
7584 

Length

Max length28
Median length22
Mean length12.353489
Min length3

Characters and Unicode

Total characters546716
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlight snow
2nd rowlight snow
3rd rowlight snow
4th rowfew clouds
5th rowsky is clear

Common Values

ValueCountFrequency (%)
sky is clear 12962
29.3%
overcast clouds 11263
25.4%
light rain 5446
12.3%
broken clouds 4416
 
10.0%
scattered clouds 2585
 
5.8%
few clouds 2217
 
5.0%
mist 1609
 
3.6%
light snow 1485
 
3.4%
moderate rain 1312
 
3.0%
snow 384
 
0.9%
Other values (9) 577
 
1.3%

Length

2024-10-19T21:26:07.447279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clouds 20481
20.6%
sky 12962
13.0%
clear 12962
13.0%
is 12962
13.0%
overcast 11263
11.3%
rain 7004
 
7.0%
light 6964
 
7.0%
broken 4416
 
4.4%
scattered 2585
 
2.6%
few 2217
 
2.2%
Other values (10) 5635
 
5.7%

Most occurring characters

ValueCountFrequency (%)
s 64035
11.7%
55195
10.1%
c 47291
 
8.7%
l 40407
 
7.4%
r 39765
 
7.3%
o 39590
 
7.2%
e 39298
 
7.2%
a 35476
 
6.5%
i 28965
 
5.3%
t 26964
 
4.9%
Other values (13) 129730
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 546716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 64035
11.7%
55195
10.1%
c 47291
 
8.7%
l 40407
 
7.4%
r 39765
 
7.3%
o 39590
 
7.2%
e 39298
 
7.2%
a 35476
 
6.5%
i 28965
 
5.3%
t 26964
 
4.9%
Other values (13) 129730
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 546716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 64035
11.7%
55195
10.1%
c 47291
 
8.7%
l 40407
 
7.4%
r 39765
 
7.3%
o 39590
 
7.2%
e 39298
 
7.2%
a 35476
 
6.5%
i 28965
 
5.3%
t 26964
 
4.9%
Other values (13) 129730
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 546716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 64035
11.7%
55195
10.1%
c 47291
 
8.7%
l 40407
 
7.4%
r 39765
 
7.3%
o 39590
 
7.2%
e 39298
 
7.2%
a 35476
 
6.5%
i 28965
 
5.3%
t 26964
 
4.9%
Other values (13) 129730
23.7%

weather_icon
Categorical

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size345.9 KiB
04n
8002 
04d
7677 
01n
6736 
01d
6226 
10d
4098 
Other values (11)
11517 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters132768
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13n
2nd row13n
3rd row13n
4th row02n
5th row01n

Common Values

ValueCountFrequency (%)
04n 8002
18.1%
04d 7677
17.3%
01n 6736
15.2%
01d 6226
14.1%
10d 4098
9.3%
10n 2846
 
6.4%
03d 1608
 
3.6%
02d 1267
 
2.9%
50n 1156
 
2.6%
03n 977
 
2.2%
Other values (6) 3663
8.3%

Length

2024-10-19T21:26:07.486635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
04n 8002
18.1%
04d 7677
17.3%
01n 6736
15.2%
01d 6226
14.1%
10d 4098
9.3%
10n 2846
 
6.4%
03d 1608
 
3.6%
02d 1267
 
2.9%
50n 1156
 
2.6%
03n 977
 
2.2%
Other values (6) 3663
8.3%

Most occurring characters

ValueCountFrequency (%)
0 42266
31.8%
d 22574
17.0%
1 22006
16.6%
n 21682
16.3%
4 15679
 
11.8%
3 4465
 
3.4%
2 2217
 
1.7%
5 1879
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 132768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 42266
31.8%
d 22574
17.0%
1 22006
16.6%
n 21682
16.3%
4 15679
 
11.8%
3 4465
 
3.4%
2 2217
 
1.7%
5 1879
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 132768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 42266
31.8%
d 22574
17.0%
1 22006
16.6%
n 21682
16.3%
4 15679
 
11.8%
3 4465
 
3.4%
2 2217
 
1.7%
5 1879
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 132768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 42266
31.8%
d 22574
17.0%
1 22006
16.6%
n 21682
16.3%
4 15679
 
11.8%
3 4465
 
3.4%
2 2217
 
1.7%
5 1879
 
1.4%

Interactions

2024-10-19T21:26:03.735426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:51.569711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:52.444241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:53.103661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:53.776645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:54.438035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:55.147263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.255815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.924229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:57.653260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:58.452731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:59.105192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:59.796711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:00.460646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:01.247676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:01.856291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:02.510297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:03.082758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:03.775922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:51.649039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:52.485424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:53.144416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:53.817142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:54.480195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:55.189180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.298001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.966974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:57.692937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:58.493257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:59.147942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:59.836991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:00.501054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:01.281610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:01.895265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-10-19T21:25:52.257033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:52.926918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:53.599509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:54.258030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:54.964531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.074425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.745824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:57.474594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:58.164057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:58.925920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:59.616103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:00.280945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:00.945072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:01.708244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:02.342071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:02.920642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:03.563622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:04.263445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:52.290172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:52.958795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:53.631783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:54.290243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:54.997521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.106256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.778747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:57.506321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:58.194761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:58.959907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:59.647969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:00.319060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:00.976848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:01.737334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:02.370413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:02.948921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:03.594436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:04.299321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:52.328727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:52.996606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:53.667938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:54.327916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:55.032936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.143579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.815726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:57.542255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:58.231018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:58.997384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:59.685624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:00.355102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:01.005497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:01.765249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:02.405364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:02.983834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:03.629213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:04.503535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:52.363483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:53.029737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:53.700550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:54.363604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:55.068255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.178412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.849107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:57.572822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:58.263133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:59.031791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:59.718866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:00.386179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:01.174005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:01.793592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:02.437869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:03.015109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:03.661582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:04.541270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:52.402952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:53.065240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:53.737796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:54.399457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:55.106217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.215723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:56.885578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:57.612365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:58.414090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:59.067257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:25:59.757554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:00.422822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:01.209004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:01.825243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:02.473627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:03.045209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-19T21:26:03.697291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-10-19T21:26:07.522572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
clouds_alldew_pointdtfeels_likehumiditypressurerain_1hrain_3hsnow_1hsnow_3htemptemp_maxtemp_mintimezonevisibilityweather_descriptionweather_iconweather_idweather_mainwind_degwind_gustwind_speed
clouds_all1.000-0.0740.007-0.2040.385-0.2770.2050.4510.1050.245-0.201-0.205-0.1930.237-0.2510.6100.5460.2180.382-0.1010.1380.162
dew_point-0.0741.0000.0870.9280.192-0.3590.1380.1430.1660.3460.9230.9190.9220.6580.0550.1540.155-0.0780.158-0.138-0.300-0.388
dt0.0070.0871.0000.0550.081-0.0210.0520.0760.0240.0840.0560.0580.0560.1560.2470.0500.0530.0320.0520.0170.0030.004
feels_like-0.2040.9280.0551.000-0.144-0.252-0.008-0.173-0.0500.0300.9950.9940.9930.7420.1240.1670.184-0.0110.172-0.092-0.348-0.455
humidity0.3850.1920.081-0.1441.000-0.3020.3930.5500.3980.565-0.166-0.171-0.1640.213-0.2570.2190.228-0.2220.231-0.1630.0200.021
pressure-0.277-0.359-0.021-0.252-0.3021.000-0.186-0.316-0.175-0.185-0.263-0.260-0.2680.2470.1470.1340.1320.1340.147-0.045-0.140-0.087
rain_1h0.2050.1380.052-0.0080.393-0.1861.0000.804NaNNaN-0.006-0.007-0.0020.080-0.1610.4140.0700.5810.078-0.1180.0990.074
rain_3h0.4510.1430.076-0.1730.550-0.3160.8041.000NaNNaN-0.175-0.178-0.1640.1590.0910.3630.1840.4740.170-0.263-0.1320.147
snow_1h0.1050.1660.024-0.0500.398-0.175NaNNaN1.0000.699-0.021-0.018-0.0130.000-0.2970.3070.0000.6910.000-0.1010.0840.098
snow_3h0.2450.3460.0840.0300.565-0.185NaNNaN0.6991.0000.1320.0980.0830.0000.2750.5710.3640.6671.000-0.1930.0060.067
temp-0.2010.9230.0560.995-0.166-0.263-0.006-0.175-0.0210.1321.0000.9990.9990.7460.1270.1770.194-0.0110.183-0.090-0.301-0.399
temp_max-0.2050.9190.0580.994-0.171-0.260-0.007-0.178-0.0180.0980.9991.0000.9970.7470.1260.1760.195-0.0110.182-0.100-0.308-0.407
temp_min-0.1930.9220.0560.993-0.164-0.268-0.002-0.164-0.0130.0830.9990.9971.0000.7410.1210.1760.195-0.0140.182-0.091-0.295-0.391
timezone0.2370.6580.1560.7420.2130.2470.0800.1590.0000.0000.7460.7470.7411.0000.1700.3120.3380.2690.2850.1860.3030.298
visibility-0.2510.0550.2470.124-0.2570.147-0.1610.091-0.2970.2750.1270.1260.1210.1701.0000.2550.1920.3040.2790.1130.080-0.017
weather_description0.6100.1540.0500.1670.2190.1340.4140.3630.3070.5710.1770.1760.1760.3120.2551.0000.6861.0001.0000.0890.1030.095
weather_icon0.5460.1550.0530.1840.2280.1320.0700.1840.0000.3640.1940.1950.1950.3380.1920.6861.0000.8940.8460.1110.1240.083
weather_id0.218-0.0780.032-0.011-0.2220.1340.5810.4740.6910.667-0.011-0.011-0.0140.2690.3041.0000.8941.0000.8940.085-0.045-0.054
weather_main0.3820.1580.0520.1720.2310.1470.0780.1700.0001.0000.1830.1820.1820.2850.2791.0000.8460.8941.0000.0930.0950.087
wind_deg-0.101-0.1380.017-0.092-0.163-0.045-0.118-0.263-0.101-0.193-0.090-0.100-0.0910.1860.1130.0890.1110.0850.0931.0000.1030.092
wind_gust0.138-0.3000.003-0.3480.020-0.1400.099-0.1320.0840.006-0.301-0.308-0.2950.3030.0800.1030.124-0.0450.0950.1031.0000.800
wind_speed0.162-0.3880.004-0.4550.021-0.0870.0740.1470.0980.067-0.399-0.407-0.3910.298-0.0170.0950.083-0.0540.0870.0920.8001.000

Missing values

2024-10-19T21:26:04.611098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-19T21:26:04.777701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-19T21:26:04.917026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

dtdt_isotimezonecity_namelatlontempvisibilitydew_pointfeels_liketemp_mintemp_maxpressuresea_levelgrnd_levelhumiditywind_speedwind_degwind_gustrain_1hrain_3hsnow_1hsnow_3hclouds_allweather_idweather_mainweather_descriptionweather_icon
015488928002019-01-31 00:00:00 +0000 UTC-18000Toronto43.653226-79.383184-17.571207.0-21.60-24.57-18.45-17.071018NaNNaN6814.9224020.06NaNNaNNaNNaN40600Snowlight snow13n
115488964002019-01-31 01:00:00 +0000 UTC-18000Toronto43.653226-79.383184-18.42402.0-22.58-25.42-19.45-18.071019NaNNaN6715.4025019.00NaNNaNNaNNaN40600Snowlight snow13n
215489000002019-01-31 02:00:00 +0000 UTC-18000Toronto43.653226-79.383184-19.243218.0-23.07-26.24-20.45-18.811019NaNNaN6917.0024020.60NaNNaNNaNNaN100600Snowlight snow13n
315489036002019-01-31 03:00:00 +0000 UTC-18000Toronto43.653226-79.383184-19.4010000.0-23.83-26.40-20.45-19.071019NaNNaN6515.4025018.00NaNNaNNaNNaN20801Cloudsfew clouds02n
415489072002019-01-31 04:00:00 +0000 UTC-18000Toronto43.653226-79.383184-19.7110000.0-24.44-26.71-21.45-19.071019NaNNaN6313.4025017.00NaNNaNNaNNaN0800Clearsky is clear01n
515489108002019-01-31 05:00:00 +0000 UTC-18000Toronto43.653226-79.383184-20.3810000.0-24.62-27.38-21.45-20.071019NaNNaN6612.4026015.40NaNNaNNaNNaN0800Clearsky is clear01n
615489144002019-01-31 06:00:00 +0000 UTC-18000Toronto43.653226-79.383184-20.3910000.0-24.63-27.39-21.20-20.071020NaNNaN6613.9026017.50NaNNaNNaNNaN0800Clearsky is clear01n
715489180002019-01-31 07:00:00 +0000 UTC-18000Toronto43.653226-79.383184-20.5410000.0-24.48-27.54-22.45-20.071020NaNNaN6813.4026016.00NaNNaNNaNNaN0800Clearsky is clear01n
815489216002019-01-31 08:00:00 +0000 UTC-18000Toronto43.653226-79.383184-20.3510000.0-24.15-27.35-21.45-20.071020NaNNaN6913.9027017.00NaNNaNNaNNaN0800Clearsky is clear01n
915489252002019-01-31 09:00:00 +0000 UTC-18000Toronto43.653226-79.383184-20.2410000.0-23.89-27.24-21.45-19.931021NaNNaN7011.3227015.43NaNNaNNaNNaN0800Clearsky is clear01n
dtdt_isotimezonecity_namelatlontempvisibilitydew_pointfeels_liketemp_mintemp_maxpressuresea_levelgrnd_levelhumiditywind_speedwind_degwind_gustrain_1hrain_3hsnow_1hsnow_3hclouds_allweather_idweather_mainweather_descriptionweather_icon
4424617040456002023-12-31 18:00:00 +0000 UTC-18000Toronto43.653226-79.3831841.7410000.0-1.03-2.821.032.341015NaNNaN815.1470NaNNaNNaNNaNNaN100804Cloudsovercast clouds04d
4424717040492002023-12-31 19:00:00 +0000 UTC-18000Toronto43.653226-79.3831841.2610000.0-0.58-3.930.892.341015NaNNaN876.1770NaNNaNNaN0.10NaN100600Snowlight snow13d
4424817040492002023-12-31 19:00:00 +0000 UTC-18000Toronto43.653226-79.3831841.2610000.0-0.58-3.930.892.341015NaNNaN876.1770NaNNaNNaN0.10NaN100500Rainlight rain10d
4424917040528002023-12-31 20:00:00 +0000 UTC-18000Toronto43.653226-79.3831841.2610000.0-0.58-3.420.892.341015NaNNaN875.1470NaNNaNNaN0.15NaN100500Rainlight rain10d
4425017040528002023-12-31 20:00:00 +0000 UTC-18000Toronto43.653226-79.3831841.2610000.0-0.58-3.420.892.341015NaNNaN875.1470NaNNaNNaN0.15NaN100600Snowlight snow13d
4425117040564002023-12-31 21:00:00 +0000 UTC-18000Toronto43.653226-79.3831841.2410000.0-0.46-3.710.542.341015NaNNaN885.6660NaNNaNNaN0.19NaN100600Snowlight snow13d
4425217040600002023-12-31 22:00:00 +0000 UTC-18000Toronto43.653226-79.3831841.1010000.0-0.72-4.130.471.791016NaNNaN876.17509.77NaNNaN0.27NaN100500Rainlight rain10n
4425317040600002023-12-31 22:00:00 +0000 UTC-18000Toronto43.653226-79.3831841.1010000.0-0.72-4.130.471.791016NaNNaN876.17509.77NaNNaN0.27NaN100600Snowlight snow13n
4425417040636002023-12-31 23:00:00 +0000 UTC-18000Toronto43.653226-79.3831840.9510000.0-0.71-5.36-0.021.791016NaNNaN888.755011.83NaNNaN0.31NaN100500Rainlight rain10n
4425517040636002023-12-31 23:00:00 +0000 UTC-18000Toronto43.653226-79.3831840.9510000.0-0.71-5.36-0.021.791016NaNNaN888.755011.83NaNNaN0.31NaN100600Snowlight snow13n